ROC curve and Neyman Pearsom Criterion
Partly based on the ECE662 Spring 2014 lecture material of Prof. Mireille Boutin.
1. Introduction and outline of the slecture
Receiver Operating Characteristic (ROC) curve is often used as an important tool to visualize the performance of a binary classifier.
- Basic knowledge of Bayes parameter estimation
- An example to illustrate the concept and properties of BPE
- The effect of sample size on the posterior
- The effect of prior on the posterior
Reference
[1] Mireille Boutin, "ECE662: Statistical Pattern Recognition and Decision Making Processes," Purdue University, Spring 2014.
[2] Jiawei Han. 2005. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.
[3] Richard O. Duda, Peter E. Hart, and David G. Stork. 2000. Pattern Classification. Wiley-Interscience.
[4] Detection Theory. http://www.ece.iastate.edu/~namrata/EE527_Spring08/l5c_2.pdf.
[5] The Neyman-Pearson Criterion. http://cnx.org/content/m11548/1.2/.
Questions and comments
If you have any questions, comments, etc. please post them on this page.
